Mineral chemistry in exploration: A review of diamond, magmatic sulfide, IOA, IOCG, and porphyry systems

Mineral chemistry has seen increased application in mineral exploration over the past decades. This review paper discusses mineral-based exploration tools for four major ore deposit types frequently encountered by early-career exploration geologists: diamondiferous kimberlites and lamproites, magmatic sulfide deposits, iron oxide apatite (IOA) and iron oxide copper gold (IOCG) systems, and porphyry Cu deposits. Traditionally, mineral chemistry has been most widely applied in diamond exploration, where Cr-rich, Ca-poor peridotitic (G10) garnets are considered to represent mantle source compositions favorable for diamond formation. Additional compositional indicators in garnet, such as Mn depletion and trace element signatures related to metasomatism, have been used to evaluate diamond potential and preservation. In the exploration for magmatic sulfide deposits, mineral chemistry proxies focus on elemental signatures that record the sulfide saturation state of mafic-ultramafic systems, such as low Ru concentrations in chromite, low Cu contents in olivine, and platinum-group element enrichments in arsenides. Further, complex zoning in pyroxene has been shown to be indicative of high magma flux in dynamic conduits in which sulfide liquids can form and accumulate. In IOA and IOCG systems, trace element systematics of magnetite, apatite, and fluorite can provide insight into ore-forming conditions, fluid sources, and (in some instances) deposit type, thereby supporting exploration efforts. Copper porphyry exploration utilizes zircon and apatite to identify magma source compositions favorable to porphyry deposit formation, while alteration minerals such as epidote and chlorite serve as vectoring tools based on systematic elemental variations with increasing distance from the porphyry center, which increases the footprint of a deposit. Significant potential exists to accelerate advances in mineral chemistry-based exploration through systematic analysis of large mineral datasets aided by machine learning. However, a current limitation is that most mineral-based exploration tools are based on empirical observations rather than thermodynamically constrained frameworks. Future progress will require interpretation of mineral chemistry within improved thermodynamic models to better constrain and refine exploration models.